Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

Intelligent vehicles are advancing at a fast speed with the improvement of automation and
connectivity, which opens up new possibilities for different cyber-attacks, including in-vehicle …

Despite learning based methods showing promising results in single view depth estimation
and visual odometry, most existing approaches treat the tasks in a supervised manner …

In this paper, we present a method for single image three-dimensional (3-D) cuboid object
detection and multiview object simultaneous localization and map** in both static and …

Various autonomous or assisted driving strategies have been facilitated through the
accurate and reliable perception of the environment around a vehicle. Among the commonly …

The progress brought by the deep learning technology over the last decade has inspired
many research domains, such as radar signal processing, speech and audio recognition …

Intelligent connected vehicles (ICVs) are believed to change people's life in the near future
by making the transportation safer, cleaner and more comfortable. Although many …

Simultaneous localization and map** (SLAM) is a widely researched topic in the field of
robotics, augmented/virtual reality and more dominantly in self-driving cars. SLAM is a …

Radar is usually more robust than the camera in severe driving scenarios, eg, weak/strong
lighting and bad weather. However, unlike RGB images captured by a camera, the semantic …

Monocular visual odometry (VO) suffers severely from error accumulation during frame-to-
frame pose estimation. In this paper, we present a self-supervised learning method for VO …